---
source_url: "https://docs.tavily.com/examples/agent-toolkit/overview"
title: Agent Toolkit - Tavily Docs
mirrored_at: 2026-08-29T13:02:45.436Z
host: docs.tavily.com
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mirror_canonical: "https://index.42a.ai/docs.tavily.com/examples/agent-toolkit/overview"
---

> **Original source:** https://docs.tavily.com/examples/agent-toolkit/overview

## What Is the Agent Toolkit?

The Tavily Agent Toolkit is a Python library that gives your agents optimized research primitives on top of the Tavily API. Instead of wiring up raw API calls, managing token limits, deduplicating sources, and formatting results for LLMs yourself, the toolkit handles all of that so your agent can focus on reasoning. It provides three layers:

Layer

What It Does

**Agents**

Pre-built research strategies that combine internal knowledge with web research. Fast or deep multi-agent modes.

**Tools**

Optimized retrieval patterns: search, crawl, extract, social media. Each tool handles context engineering (formatting, dedup, token management) automatically.

**Bring Your Own Model**

Every tool that needs an LLM accepts a `ModelConfig`. Supports 20+ providers via LangChain with automatic fallback chains.

## Installation

For LLM features, install your preferred provider:

## Available Tools

Tool

When to Use

`search_and_answer`

Answer questions with web research + LLM synthesis

`search_dedup`

Run multiple queries in parallel, deduplicate results

`crawl_and_summarize`

Extract and summarize entire websites

`extract_and_summarize`

Get focused summaries from specific URLs

`social_media_search`

Search Reddit, X, LinkedIn, TikTok, and more

## Pre-Built Agents

### `hybrid_research`

Combines your internal knowledge base with real-time web research. You provide a RAG function that queries your internal data — the agent identifies gaps and fills them with web research. Two modes:

Mode

Best For

How It Works

**Fast**

Quick answers, lower latency

Internal RAG → generate subqueries → parallel web search → synthesize

**Multi-Agent**

Comprehensive research, complex topics

Internal RAG → identify gaps → Tavily deep research endpoint → synthesize

## Model Configuration

All tools accept a `ModelConfig` for LLM operations. Use the `"provider:model"` format:

20+ providers are supported via LangChain’s `init_chat_model`: OpenAI, Anthropic, Google, Groq, Mistral, Cohere, Together, Fireworks, AWS Bedrock, Azure, and more.

## Use-Case Recipes

Production-ready agent implementations. Each is available in both Anthropic SDK and LangGraph flavors.